多传感器量测自适应Rao-Blackwellised粒子滤波算法

针对量测不确定下非线性系统状态估计中多传感器量测数据的有效利用和计算复杂度的简化问题，给出了一种多传感器量测自适应Rao-Blackwellised粒子滤波算法。首先，通过随机采样策略和量测模型先验转移概率实现用于评估粒子权重的传感器有效量测集合的采样；其次，利用重采样步骤和概率最大化原则完成对不含扰动影响传感器量测模型的辨识;最终，依据Rao-Blackwellised粒子滤波中非线性状态分量和线性状态分量的独立求解方式实现当前时刻系统的状态估计。理论分析和仿真实验结果验证了算法的可行性和有效性。

Multi-sensor observation of adaptive Rao-Blackwellised particle filtering algorithm

Aiming at the effective utilization of multi-sensor observation and the simplification of computational complexity for the state estimation of nonlinear systems in observation uncertainty, a novel multi-sensor observation adaptive Rao-Blackwellised particle filtering algorithm is proposed. Firstly, in the new algorithm, the effective sensor observation set used to measure particle weight is sampled by means of the random sampling strategy and the observation model prior transition probability. Then the identification of the sensor observation model without the influence of disturbance is realized on the basis of the re-sampling steps and the probability maximization principle. Finally, according to the independently solving way of nonlinear state component and linear state component in Rao-Blackwellised particle filter, the system state estimation is achieved at current time. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.